First author: 16
(note: 9 first author manuscripts are in review. see pre-prints.)
Williams, D. R. (in press). Learning to Live with Sampling Variability: Expected Replicability in Partial Correlation Networks. Psychological Methods.
Williams, D. R. (2021) Bayesian Estimation for Gaussian Graphical Models: Structure Learning, Predictability, and Network Comparisons, Multivariate Behavioral Research, 56:2, 336-352, DOI: 10.1080/00273171.2021.1894412
Williams, D. R., Martin, S. R., & Rast, P. (in press). Putting the Individual into Reliability: Bayesian Testing of Homogeneous Within-Person Variance in Hierarchical Models. Behavior Research Methods.
Williams, D. R., Liu, S., Martin, S. R., & Rast, P. (2021). Bayesian Multivariate Mixed-Effects Location Scale Modeling of Longitudinal Relations among Affective Traits, States, and Physical Activity. European Journal of Psychological Assessment. 36 (6). DOI: 10.1027/1015-5759/a000624
Mulder, J., Williams, D. R., Gu, X., Olsson-Collentine, A., Tomarken, A., Boing-Messing, F., Hoijtink, H., … & van Lissa, C. (in press). BFpack: Flexible bayes factor testing of scientific theories in R. Journal of Statistical Software.
Williams, D. R., Mulder, J., Rouder, J. N., & Rast, P. (2020). Beneath the surface: Unearthing within-person variability and mean relations with Bayesian mixed models. Psychological Methods. DOI: 10.1037/met0000270
These analyses are reproduced at the GitHub repo.
Williams, D. R. & Joris Mulder. Bayesian Hypothesis Testing for Gaussian Graphical Models: Conditional Independence and Order Constraints. Journal of Mathematical Psychology, 99, 102441. DOI: 10.1016/j.jmp.2020.102441`
Williams, D. R., Rast, P., Pericchi, L. R., & Mulder, J. (2020). Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection. Psychological methods. DOI: 10.1037/met0000254`
Williams, D. R., & Rast, P. (2020). Back to the basics: Rethinking partial correlation network methodology. British Journal of Mathematical and Statistical Psychology, 73(2), 187-212. DOI: 10.1111/bmsp.12173`
Williams, D. R., & Mulder, J. (2020). BGGM: Bayesian Gaussian Graphical Models in R. Journal of Open Source Software, 5(51), 2111. DOI: 10.21105/joss.02111
Williams, D. R., & Burkner, P. (2020). Coding errors lead to unsupported conclusions: a critique of Hofmann et al. (2015). Meta-Psychology, 4. DOI: 10.15626/MP.2018.872.
Briganti, G., Williams, D. R., Mulder, J., & Linkowski, P. (2020). Bayesian network structure and predictability of autistic traits. Psychological Reports. DOI: 10.1177/0033294120978159.
Jones, P. J., Williams, D. R., & McNally, R. J. (2020). Sampling variability is not nonreplication: A Bayesian reanalysis of Forbes, Wright, Markon, and Krueger. Multivariate Behavioral Research, 1-7. DOI: 10.1080/00273171.2020.1797460
Rast, P., Martin, S. R., Liu, S., & Williams, D. R. (2020). A new frontier for studying within-person variability: Bayesian multivariate generalized autoregressive conditional heteroskedasticity models. Psychological Methods. DOI: 10.1037/met0000357`
Williams, D. R., Zimprich, D. R., & Rast, P. (2019). A Bayesian nonlinear mixed-effects location scale model for learning. Behavior research methods, 51(5), 1968-1986. DOI: 10.3758/s13428-019-01255-9.
Williams, D. R., Rhemtulla, M., Wysocki, A. C., & Rast, P. (2019). On nonregularized estimation of psychological networks. Multivariate behavioral research, 54(5), 719-750. DOI: 10.1080/00273171.2019.1575716.
Nalborczyk, L., Burkner, P. C., & Williams, D. R. (2019). Pragmatism should not be a substitute for statistical literacy, a commentary on Albers, Kiers, and van Ravenzwaaij (2019). Collabra: Psychology, 5(1). DOI: 10.1525/collabra.197
Quintana, D. S., & Williams, D. R. (2018). Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP. BMC psychiatry, 18(1), 178. DOI: 10.1186/s12888-018-1761-4
Lakens, D., Adol , F. G., Albers, C. J., Anvari, F., Apps, M. A., Argamon, S. E., … & Buchanan, E. M. (2018). Justify your alpha. Nature Human Behaviour, 2(3), 168.
Carlsson, R., Agerstrom, J., Williams, D.R, & Burns, G. N. (2018). A Primer on the benenits of differential treatment analysis when predicting discriminatory behavior. Quantitative Methods for Psychology, 14(3), 193-198. DOI: 10.20982/tqmp.14.3.p193.
Merritt, J. R., Davis, M. T., Jalabert, C., Libecap, T. J., Williams, D. R., Soma, K. K., & Maney, D. L. (2018). Rapid effects of estradiol on aggression depend on genotype in a species with an estrogen receptor polymorphism. Hormones and behavior, 98, 210-218. DOI: 10.1016/j.yhbeh.2017.11.014`
Williams, D. R., Carlsson, R., & Burkner, P. C. (2017). Between-litter variation in developmental studies of hormones and behavior: In ated false positives and diminished power. Frontiers in neuroendocrinology, 47, 154-166. DOI: 10.1016/j.yfrne.2017.08.003
Williams, D. R., & Burkner, P. C. (2017). Effects of intranasal oxytocin on symptoms of schizophrenia: a multivariate Bayesian meta-analysis. Psychoneuroendocrinology, 75, 141-151. DOI: 10.1016/j.psyneuen.2016.10.013
Burkner, P. C., Williams, D. R.\(^*\), Simmons, T. C., & Woolley, J. D. (2017). Intranasal oxytocin may improve high-level social cognition in Schizophrenia, but not social cognition or neurocognition in general: a multilevel bayesian meta-analysis. Schizophrenia Bulletin, 43(6), 1291-1303. DOI: 10.1093/schbul/sbx053`
\(^*\) shared 1st authorship
Williams, D. R., & Burkner, P. C. (2017). Data extraction and statistical errors: A quantitative critique of Gumley, Braehler, and Macbeth (2014). British Journal of Clinical Psychology, 56(2), 208-211. DOI: 10.1111/bjc.12130
Carlsson, R., Schimmack, U., Williams, D. R., & Burkner, P. C. (2017). Bayes factors from pooled data are no substitute for Bayesian meta-Analysis: commentary on Scheibehenne, Jamil, and Wagenmakers (2016). Psychological science, 28(11), 1694-1697. DOI: 10.1177/0956797616684682`
Maninger, N., Mendoza, S. P., Williams, D. R., Mason, W. A., Cherry, S. R., Rowland, D. J., … & Bales, K. L. (2017). Imaging, behavior and endocrine analysis of “Jealousy” in a monogamous primate. Frontiers in ecology and evolution, 5, 119. DOI: 10.3389/fevo.2017.00119
Bales, K. L., del Razo, R. A., Conklin, Q. A., Hartman, S., Mayer, H. S., Rogers, F. D., … & Witczak, L. R. (2017). Focus: Comparative medicine: Titi monkeys as a novel non-human primate model for the neurobiology of pair bonding. The Yale journal of biology and medicine, 90(3), 373.
First author: 9
Highlights (all first author)
(note: status provided below.)
Williams, D. R., Rodriguez, J. E., & Burkner, P. (2021). Putting Variation into Variance: Modeling Between-Study Heterogeneity in Meta-Analysis. PsyArXiv. DOI: 10.31234/osf.io/9vkqy
Psychological Methods: submitted
The code to reproduce the analyses is on GitHub.
Williams, D. R. (2021). Many Mixture Components, Oh My: Extending the Spike and Slab to Bayesian Hypothesis Testing with Multinoulli Indicators. PsyArXiv. DOI: 10.31234/osf.io/xcvby
Behavior Research Methods: in review
Williams, D. R. (2021). GGMnonreg: Non-Regularized Gaussian Graphical Models in R. PsyArXiv. DOI: 10.31234/osf.io/p5jk9.
Journal of Open-Source Software: in review
Williams, D. R. (2021). The Confidence Interval that Wasn’t: Bootstrapped Confidence Intervals” in L1-Regularized Partial Correlation Networks. PsyArXiv. DOI: 10.31234/osf.io/kjh2f`
Psychological Methods: submitted
The code to reproduce the simulations is on GitHub.
Williams, D. R., Briganti, G., Linkowski, P., & Mulder, J. (2021). On Accepting the Null Hypothesis of Conditional Independence in Partial Correlation Networks: A Bayesian Analysis. PsyArXiv. DOI: 10.31234/osf.io/7uhx8
Multivariate Behavioral Research: in review
Rodriguez, J. E., Williams, D. R., & Rast, P. (2021). Who Is and Is Not “Average”? Random Effects Selection with Spike-and-Slab Priors. PsyArXiv. DOI: 10.31234/osf.io/4d9tv
Psychological Methods: in review
Rodriguez, J. E., & Williams, D. R. (2021). Painless Posterior Sampling: Bayesian Bootstrapped Correlation Coefficients. PsyArXiv. DOI: 10.31234/osf.io/v2chs
The Quantitative Methods for Psychology: in review
Jongerling, J., Epskamp, S., & Williams, D. R. (2021). Bayesian Uncertainty Estimation for Gaussian Graphical Models and Centrality Indices. PsyArXiv. DOI: 10.31234/osf.io/7kude
Multivariate Behavioral Research: in revision
Williams, D. R., & Rodriguez, J. (2020). Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks. PsyArXiv. DOI: 10.31234/osf.io/8pr9b`
Psychological Methods: revision
Williams, D. R., Martin, S. R., DeBolt, M., Oakes, L., & Rast, P. (2020). A fine-tooth comb for measurement reliability: Predicting true score and error variance in hierarchical models. PsyArXiv. DOI: 10.31234/osf.io/2ux7t`
Multivariate Behavioral Research: in review
Williams, D. R. (2020). GGMncv: Nonconvex Penalized Gaussian Graphical Models in R. PsyArXiv. DOI: 10.31234/osf.io/6jz5m`
R Journal: submitted
Williams, D. R. (2020). Beyond Lasso: A Survey of Nonconvex Regularization in Gaussian Graphical Models. PsyArXiv. DOI: 10.31234/osf.io/ad57p
Psychometrika: in review
Heck, D. W., Boehm, U., Boing-Messing, F., Burkner, P. C., Derks, K., Dienes, Z., … & Hoijtink, H. (2020). A Review of Applications of the Bayes Factor in Psychological Research. PsyArXiv.
Psychological Methods: in review
Williams, D. R., Rast, P., & Burkner, P. C. (2018). Bayesian Meta-Analysis with Weakly Informative Prior Distributions. PsyArXiv.
Williams, D. R. (2020). vICC: Varying Intraclass Correlation Coefficients in R. PsyArXiv. DOI: 10.31234/osf.io/95de4.
Rodriguez, J. E., Williams, D. R., Rast, P., & Mulder, J. (2020). On formalizing theoretical expectations: Bayesian testing of central structures in psychological networks. PsyArXiv. DOI: 10.31234/osf.io/zw7pf
Martin, S. R., Williams, D. R., & Rast, P. (2019). Measurement invariance assessment with Bayesian hierarchical inclusion modeling. PsyArXiv. DOI: 10.31234/osf.io/qbdjt
Williams, D. R., Piironen, J., Vehtari, A., & Rast, P. (2018). Bayesian estimation of Gaussian graphical models with predictive covariance selection. arXiv preprint arXiv:1801.05725v5.
Williams, D. R., & Martin, S. R. (2017). Rethinking robust statistics with modern Bayesian methods. PsyArXiv. DOI: 10.31234/osf.io/vaw38.
Martin, S. R., & Williams, D. R. (2017). Outgrowing the Procrustean Bed of Normality: The Utility of Bayesian Modeling for Asymmetrical Data Analysis. PsyArXiv. DOI: 10.31234/osf.io/26m49
If you see mistakes or want to suggest changes, please create an issue on the source repository.